import inspect
from typing import Optional, Union

import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.utils import (
    PIL_INTERPOLATION,
    randn_tensor,
)
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase					):
   if isinstance(lowercase							,				torch.Tensor					):
      return image
   elif isinstance(lowercase							,				PIL.Image.Image					):
      lowerCAmelCase_:							int       =	[image]

   if isinstance(image[0]							,				PIL.Image.Image					):
      lowerCAmelCase_:							List[Any]       =	[np.array(i.resize((w, h)							,				resample=PIL_INTERPOLATION["lanczos"]					)					)[None, :] for i in image]
      lowerCAmelCase_:							int       =	np.concatenate(lowercase							,				axis=0					)
      lowerCAmelCase_:							Tuple       =	np.array(lowercase					).astype(np.floataa					) / 255.0
      lowerCAmelCase_:							Dict       =	image.transpose(0							,				3							,				1							,				2					)
      lowerCAmelCase_:							List[Any]       =	2.0 * image - 1.0
      lowerCAmelCase_:							int       =	torch.from_numpy(lowercase					)
   elif isinstance(image[0]							,				torch.Tensor					):
      lowerCAmelCase_:							Any       =	torch.cat(lowercase							,				dim=0					)
   return image
def 		snake_case__	(		lowercase							,				lowercase							,				lowercase							,				lowercase=0.9995					):
   if not isinstance(lowercase							,				np.ndarray					):
      lowerCAmelCase_:							Optional[int]       =	True
      lowerCAmelCase_:							List[Any]       =	va.device
      lowerCAmelCase_:							List[str]       =	va.cpu().numpy()
      lowerCAmelCase_:							Union[str, Any]       =	va.cpu().numpy()

   lowerCAmelCase_:							str       =	np.sum(va * va / (np.linalg.norm(lowercase					) * np.linalg.norm(lowercase					))					)
   if np.abs(lowercase					) > DOT_THRESHOLD:
      lowerCAmelCase_:							Optional[int]       =	(1 - t) * va + t * va
   else:
      lowerCAmelCase_:							Any       =	np.arccos(lowercase					)
      lowerCAmelCase_:							List[str]       =	np.sin(lowercase					)
      lowerCAmelCase_:							Optional[Any]       =	theta_a * t
      lowerCAmelCase_:							List[str]       =	np.sin(lowercase					)
      lowerCAmelCase_:							List[str]       =	np.sin(theta_a - theta_t					) / sin_theta_a
      lowerCAmelCase_:							int       =	sin_theta_t / sin_theta_a
      lowerCAmelCase_:							Dict       =	sa * va + sa * va

   if inputs_are_torch:
      lowerCAmelCase_:							int       =	torch.from_numpy(lowercase					).to(lowercase					)

   return va
def 		snake_case__	(		lowercase							,				lowercase					):
   lowerCAmelCase_:							Tuple       =	F.normalize(lowercase							,				dim=-1					)
   lowerCAmelCase_:							Optional[Any]       =	F.normalize(lowercase							,				dim=-1					)
   return (x - y).norm(dim=-1					).div(2					).arcsin().pow(2					).mul(2					)
def 		snake_case__	(		lowercase							,				lowercase					):
   for param in model.parameters():
      lowerCAmelCase_:							List[Any]       =	value







class 					_lowercase    (     snake_case_			):






    '''simple docstring'''




    def __init__(    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__=None       ,						lowerCamelCase__=None       ,						lowerCamelCase__=None       ,						):
       super().__init__()
       self.register_modules(
           vae=lowerCamelCase__       ,						text_encoder=lowerCamelCase__       ,						clip_model=lowerCamelCase__       ,						tokenizer=lowerCamelCase__       ,						unet=lowerCamelCase__       ,						scheduler=lowerCamelCase__       ,						feature_extractor=lowerCamelCase__       ,						coca_model=lowerCamelCase__       ,						coca_tokenizer=lowerCamelCase__       ,						coca_transform=lowerCamelCase__       ,						)
       lowerCAmelCase_:							List[str]       =	(
           feature_extractor.size
           if isinstance(feature_extractor.size       ,						lowerCamelCase__ )
           else feature_extractor.size["shortest_edge"]
       )
       lowerCAmelCase_:							Optional[int]       =	transforms.Normalize(mean=feature_extractor.image_mean       ,						std=feature_extractor.image_std )
       set_requires_grad(self.text_encoder       ,						lowerCamelCase__ )
       set_requires_grad(self.clip_model       ,						lowerCamelCase__ )




    def      _a    (    self       ,						lowerCamelCase__ = "auto" ):
       if slice_size == "auto":
          # half the attention head size is usually a good trade-off between
          # speed and memory
          lowerCAmelCase_:							List[Any]       =	self.unet.config.attention_head_dim // 2
       self.unet.set_attention_slice(lowerCamelCase__ )




    def      _a    (    self ):
       self.enable_attention_slicing(lowerCamelCase__ )




    def      _a    (    self ):
       set_requires_grad(self.vae       ,						lowerCamelCase__ )




    def      _a    (    self ):
       set_requires_grad(self.vae       ,						lowerCamelCase__ )




    def      _a    (    self ):
       set_requires_grad(self.unet       ,						lowerCamelCase__ )




    def      _a    (    self ):
       set_requires_grad(self.unet       ,						lowerCamelCase__ )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       # get the original timestep using init_timestep
       lowerCAmelCase_:							Union[str, Any]       =	min(int(num_inference_steps * strength )       ,						lowerCamelCase__ )

       lowerCAmelCase_:							Dict       =	max(num_inference_steps - init_timestep       ,						0 )
       lowerCAmelCase_:							int       =	self.scheduler.timesteps[t_start:]

       return timesteps, num_inference_steps - t_start




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__=None ):
       if not isinstance(lowerCamelCase__       ,						torch.Tensor ):
          raise ValueError(F'''`image` has to be of type `torch.Tensor` but is {type(lowerCamelCase__ )}''' )

       lowerCAmelCase_:							int       =	image.to(device=lowerCamelCase__       ,						dtype=lowerCamelCase__ )

       if isinstance(lowerCamelCase__       ,						lowerCamelCase__ ):
          lowerCAmelCase_:							List[Any]       =	[
              self.vae.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(lowerCamelCase__ )
          ]
          lowerCAmelCase_:							Union[str, Any]       =	torch.cat(lowerCamelCase__       ,						dim=0 )
       else:
          lowerCAmelCase_:							Union[str, Any]       =	self.vae.encode(lowerCamelCase__ ).latent_dist.sample(lowerCamelCase__ )

       # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
       lowerCAmelCase_:							Optional[Any]       =	0.1_8_2_1_5 * init_latents
       lowerCAmelCase_:							List[Any]       =	init_latents.repeat_interleave(lowerCamelCase__       ,						dim=0 )

       lowerCAmelCase_:							str       =	randn_tensor(init_latents.shape       ,						generator=lowerCamelCase__       ,						device=lowerCamelCase__       ,						dtype=lowerCamelCase__ )

       # get latents
       lowerCAmelCase_:							Optional[Any]       =	self.scheduler.add_noise(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )
       lowerCAmelCase_:							Union[str, Any]       =	init_latents

       return latents




    def      _a    (    self       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							Optional[Any]       =	self.coca_transform(lowerCamelCase__ ).unsqueeze(0 )
       with torch.no_grad(), torch.cuda.amp.autocast():
          lowerCAmelCase_:							int       =	self.coca_model.generate(transformed_image.to(device=self.device       ,						dtype=self.coca_model.dtype ) )
       lowerCAmelCase_:							List[Any]       =	self.coca_tokenizer.decode(generated[0].cpu().numpy() )
       return generated.split("<end_of_text>" )[0].replace("<start_of_text>"       ,						"" ).rstrip(" .," )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
       lowerCAmelCase_:							int       =	self.feature_extractor.preprocess(lowerCamelCase__ )
       lowerCAmelCase_:							Optional[int]       =	torch.from_numpy(clip_image_input["pixel_values"][0] ).unsqueeze(0 ).to(self.device ).half()
       lowerCAmelCase_:							List[str]       =	self.clip_model.get_image_features(lowerCamelCase__ )
       lowerCAmelCase_:							Dict       =	image_embeddings_clip / image_embeddings_clip.norm(p=2       ,						dim=-1       ,						keepdim=lowerCamelCase__ )
       lowerCAmelCase_:							Optional[Any]       =	image_embeddings_clip.repeat_interleave(lowerCamelCase__       ,						dim=0 )
       return image_embeddings_clip




    @torch.enable_grad()
    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						):
       lowerCAmelCase_:							str       =	latents.detach().requires_grad_()

       lowerCAmelCase_:							Optional[int]       =	self.scheduler.scale_model_input(lowerCamelCase__       ,						lowerCamelCase__ )

       # predict the noise residual
       lowerCAmelCase_:							Union[str, Any]       =	self.unet(lowerCamelCase__       ,						lowerCamelCase__       ,						encoder_hidden_states=lowerCamelCase__ ).sample

       if isinstance(self.scheduler       ,						(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler) ):
          lowerCAmelCase_:							Any       =	self.scheduler.alphas_cumprod[timestep]
          lowerCAmelCase_:							Union[str, Any]       =	1 - alpha_prod_t
          # compute predicted original sample from predicted noise also called
          # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
          lowerCAmelCase_:							Dict       =	(latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

          lowerCAmelCase_:							Optional[Any]       =	torch.sqrt(lowerCamelCase__ )
          lowerCAmelCase_:							Any       =	pred_original_sample * (fac) + latents * (1 - fac)
       elif isinstance(self.scheduler       ,						lowerCamelCase__ ):
          lowerCAmelCase_:							str       =	self.scheduler.sigmas[index]
          lowerCAmelCase_:							Optional[Any]       =	latents - sigma * noise_pred
       else:
          raise ValueError(F'''scheduler type {type(self.scheduler )} not supported''' )

       # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
       lowerCAmelCase_:							Any       =	1 / 0.1_8_2_1_5 * sample
       lowerCAmelCase_:							Union[str, Any]       =	self.vae.decode(lowerCamelCase__ ).sample
       lowerCAmelCase_:							Union[str, Any]       =	(image / 2 + 0.5).clamp(0       ,						1 )

       lowerCAmelCase_:							List[Any]       =	transforms.Resize(self.feature_extractor_size )(lowerCamelCase__ )
       lowerCAmelCase_:							Dict       =	self.normalize(lowerCamelCase__ ).to(latents.dtype )

       lowerCAmelCase_:							Tuple       =	self.clip_model.get_image_features(lowerCamelCase__ )
       lowerCAmelCase_:							List[Any]       =	image_embeddings_clip / image_embeddings_clip.norm(p=2       ,						dim=-1       ,						keepdim=lowerCamelCase__ )

       lowerCAmelCase_:							Any       =	spherical_dist_loss(lowerCamelCase__       ,						lowerCamelCase__ ).mean() * clip_guidance_scale

       lowerCAmelCase_:							str       =	-torch.autograd.grad(lowerCamelCase__       ,						lowerCamelCase__ )[0]

       if isinstance(self.scheduler       ,						lowerCamelCase__ ):
          lowerCAmelCase_:							List[str]       =	latents.detach() + grads * (sigma**2)
          lowerCAmelCase_:							Optional[Any]       =	noise_pred_original
       else:
          lowerCAmelCase_:							List[Any]       =	noise_pred_original - torch.sqrt(lowerCamelCase__ ) * grads
       return noise_pred, latents




    @torch.no_grad()
    def __call__(    self       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = 512       ,						lowerCamelCase__ = 512       ,						lowerCamelCase__ = 0.6       ,						lowerCamelCase__ = 50       ,						lowerCamelCase__ = 7.5       ,						lowerCamelCase__ = 1       ,						lowerCamelCase__ = 0.0       ,						lowerCamelCase__ = 100       ,						lowerCamelCase__ = None       ,						lowerCamelCase__ = "pil"       ,						lowerCamelCase__ = True       ,						lowerCamelCase__ = 0.8       ,						lowerCamelCase__ = 0.1       ,						lowerCamelCase__ = 0.1       ,						):
       if isinstance(lowerCamelCase__       ,						lowerCamelCase__ ) and len(lowerCamelCase__ ) != batch_size:
          raise ValueError(F'''You have passed {batch_size} batch_size, but only {len(lowerCamelCase__ )} generators.''' )

       if height % 8 != 0 or width % 8 != 0:
          raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.''' )

       if isinstance(lowerCamelCase__       ,						torch.Generator ) and batch_size > 1:
          lowerCAmelCase_:							Any       =	[generator] + [None] * (batch_size - 1)

       lowerCAmelCase_:							List[str]       =	[
           ("model", self.coca_model is None),
           ("tokenizer", self.coca_tokenizer is None),
           ("transform", self.coca_transform is None),
       ]
       lowerCAmelCase_:							Union[str, Any]       =	[x[0] for x in coca_is_none if x[1]]
       lowerCAmelCase_:							List[Any]       =	", ".join(lowerCamelCase__ )
       # generate prompts with coca model if prompt is None
       if content_prompt is None:
          if len(lowerCamelCase__ ):
             raise ValueError(
                 F'''Content prompt is None and CoCa [{coca_is_none_str}] is None.'''
                 F'''Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''' )
          lowerCAmelCase_:							Tuple       =	self.get_image_description(lowerCamelCase__ )
       if style_prompt is None:
          if len(lowerCamelCase__ ):
             raise ValueError(
                 F'''Style prompt is None and CoCa [{coca_is_none_str}] is None.'''
                 F''' Set prompt or pass Coca [{coca_is_none_str}] to DiffusionPipeline.''' )
          lowerCAmelCase_:							List[str]       =	self.get_image_description(lowerCamelCase__ )

       # get prompt text embeddings for content and style
       lowerCAmelCase_:							Dict       =	self.tokenizer(
           lowerCamelCase__       ,						padding="max_length"       ,						max_length=self.tokenizer.model_max_length       ,						truncation=lowerCamelCase__       ,						return_tensors="pt"       ,						)
       lowerCAmelCase_:							List[Any]       =	self.text_encoder(content_text_input.input_ids.to(self.device ) )[0]

       lowerCAmelCase_:							Optional[Any]       =	self.tokenizer(
           lowerCamelCase__       ,						padding="max_length"       ,						max_length=self.tokenizer.model_max_length       ,						truncation=lowerCamelCase__       ,						return_tensors="pt"       ,						)
       lowerCAmelCase_:							List[str]       =	self.text_encoder(style_text_input.input_ids.to(self.device ) )[0]

       lowerCAmelCase_:							int       =	slerp(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )

       # duplicate text embeddings for each generation per prompt
       lowerCAmelCase_:							Any       =	text_embeddings.repeat_interleave(lowerCamelCase__       ,						dim=0 )

       # set timesteps
       lowerCAmelCase_:							int       =	"offset" in set(inspect.signature(self.scheduler.set_timesteps ).parameters.keys() )
       lowerCAmelCase_:							Optional[Any]       =	{}
       if accepts_offset:
          lowerCAmelCase_:							Union[str, Any]       =	1

       self.scheduler.set_timesteps(lowerCamelCase__       ,						**lowerCamelCase__ )
       # Some schedulers like PNDM have timesteps as arrays
       # It's more optimized to move all timesteps to correct device beforehand
       self.scheduler.timesteps.to(self.device )

       lowerCAmelCase_  ,	lowerCAmelCase_:							int       =	self.get_timesteps(lowerCamelCase__       ,						lowerCamelCase__       ,						self.device )
       lowerCAmelCase_:							Tuple       =	timesteps[:1].repeat(lowerCamelCase__ )

       # Preprocess image
       lowerCAmelCase_:							Tuple       =	preprocess(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )
       lowerCAmelCase_:							int       =	self.prepare_latents(
           lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						text_embeddings.dtype       ,						self.device       ,						lowerCamelCase__ )

       lowerCAmelCase_:							Optional[int]       =	preprocess(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )
       lowerCAmelCase_:							List[Any]       =	self.prepare_latents(
           lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						text_embeddings.dtype       ,						self.device       ,						lowerCamelCase__ )

       lowerCAmelCase_:							Union[str, Any]       =	slerp(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )

       if clip_guidance_scale > 0:
          lowerCAmelCase_:							List[Any]       =	self.get_clip_image_embeddings(lowerCamelCase__       ,						lowerCamelCase__ )
          lowerCAmelCase_:							Optional[int]       =	self.get_clip_image_embeddings(lowerCamelCase__       ,						lowerCamelCase__ )
          lowerCAmelCase_:							str       =	slerp(
              lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )

       # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
       # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
       # corresponds to doing no classifier free guidance.
       lowerCAmelCase_:							Dict       =	guidance_scale > 1.0
       # get unconditional embeddings for classifier free guidance
       if do_classifier_free_guidance:
          lowerCAmelCase_:							List[Any]       =	content_text_input.input_ids.shape[-1]
          lowerCAmelCase_:							Optional[int]       =	self.tokenizer([""]       ,						padding="max_length"       ,						max_length=lowerCamelCase__       ,						return_tensors="pt" )
          lowerCAmelCase_:							Dict       =	self.text_encoder(uncond_input.input_ids.to(self.device ) )[0]
          # duplicate unconditional embeddings for each generation per prompt
          lowerCAmelCase_:							List[Any]       =	uncond_embeddings.repeat_interleave(lowerCamelCase__       ,						dim=0 )

          # For classifier free guidance, we need to do two forward passes.
          # Here we concatenate the unconditional and text embeddings into a single batch
          # to avoid doing two forward passes
          lowerCAmelCase_:							int       =	torch.cat([uncond_embeddings, text_embeddings] )

       # get the initial random noise unless the user supplied it

       # Unlike in other pipelines, latents need to be generated in the target device
       # for 1-to-1 results reproducibility with the CompVis implementation.
       # However this currently doesn't work in `mps`.
       lowerCAmelCase_:							Dict       =	(batch_size, self.unet.config.in_channels, height // 8, width // 8)
       lowerCAmelCase_:							List[Any]       =	text_embeddings.dtype
       if latents is None:
          if self.device.type == "mps":
             # randn does not work reproducibly on mps
             lowerCAmelCase_:							List[Any]       =	torch.randn(lowerCamelCase__       ,						generator=lowerCamelCase__       ,						device="cpu"       ,						dtype=lowerCamelCase__ ).to(
                 self.device )
          else:
             lowerCAmelCase_:							List[Any]       =	torch.randn(lowerCamelCase__       ,						generator=lowerCamelCase__       ,						device=self.device       ,						dtype=lowerCamelCase__ )
       else:
          if latents.shape != latents_shape:
             raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}''' )
          lowerCAmelCase_:							Optional[int]       =	latents.to(self.device )

       # scale the initial noise by the standard deviation required by the scheduler
       lowerCAmelCase_:							Dict       =	latents * self.scheduler.init_noise_sigma

       # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
       # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
       # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
       # and should be between [0, 1]
       lowerCAmelCase_:							Dict       =	"eta" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
       lowerCAmelCase_:							Optional[int]       =	{}
       if accepts_eta:
          lowerCAmelCase_:							Optional[int]       =	eta

       # check if the scheduler accepts generator
       lowerCAmelCase_:							int       =	"generator" in set(inspect.signature(self.scheduler.step ).parameters.keys() )
       if accepts_generator:
          lowerCAmelCase_:							str       =	generator

       with self.progress_bar(total=lowerCamelCase__ ):
          for i, t in enumerate(lowerCamelCase__ ):
             # expand the latents if we are doing classifier free guidance
             lowerCAmelCase_:							Tuple       =	torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
             lowerCAmelCase_:							Optional[Any]       =	self.scheduler.scale_model_input(lowerCamelCase__       ,						lowerCamelCase__ )

             # predict the noise residual
             lowerCAmelCase_:							Dict       =	self.unet(lowerCamelCase__       ,						lowerCamelCase__       ,						encoder_hidden_states=lowerCamelCase__ ).sample

             # perform classifier free guidance
             if do_classifier_free_guidance:
                lowerCAmelCase_  ,	lowerCAmelCase_:							Optional[int]       =	noise_pred.chunk(2 )
                lowerCAmelCase_:							Any       =	noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

             # perform clip guidance
             if clip_guidance_scale > 0:
                lowerCAmelCase_:							Dict       =	(
                    text_embeddings.chunk(2 )[1] if do_classifier_free_guidance else text_embeddings
                )
                lowerCAmelCase_  ,	lowerCAmelCase_:							Optional[int]       =	self.cond_fn(
                    lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						)

             # compute the previous noisy sample x_t -> x_t-1
             lowerCAmelCase_:							Union[str, Any]       =	self.scheduler.step(lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						**lowerCamelCase__ ).prev_sample

        # Hardcode 0.18215 because stable-diffusion-2-base has not self.vae.config.scaling_factor
       lowerCAmelCase_:							Union[str, Any]       =	1 / 0.1_8_2_1_5 * latents
       lowerCAmelCase_:							Union[str, Any]       =	self.vae.decode(lowerCamelCase__ ).sample

       lowerCAmelCase_:							List[str]       =	(image / 2 + 0.5).clamp(0       ,						1 )
       lowerCAmelCase_:							Optional[Any]       =	image.cpu().permute(0       ,						2       ,						3       ,						1 ).numpy()

       if output_type == "pil":
          lowerCAmelCase_:							int       =	self.numpy_to_pil(lowerCamelCase__ )

       if not return_dict:
          return (image, None)

       return StableDiffusionPipelineOutput(images=lowerCamelCase__       ,						nsfw_content_detected=lowerCamelCase__ )
"""simple docstring"""



def 	_lowerCamelCase(	a				):
       return " ".join(
           "".join(word[::-1]				) if len(a				) > 4 else word for word in sentence.split()				)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(reverse_long_words("""Hey wollef sroirraw"""))
